def test_simple_run(self):
""" Method to test a simple 1 cpu call with the scheduler.
"""
# Configure the environment
study_config = StudyConfig(
modules=[],
use_smart_caching=True,
output_directory=self.outdir,
number_of_cpus=1,
generate_logging=True,
use_scheduler=True)
# Create pipeline
pipeline = get_process_instance(self.pipeline_name)
pipeline.date_in_filename = True
# Set pipeline input parameters
dicom_dataset = get_sample_data("dicom")
dcmfolder = os.path.join(self.outdir, "dicom")
if not os.path.isdir(dcmfolder):
os.makedirs(dcmfolder)
shutil.copy(dicom_dataset.barre, os.path.join(dcmfolder, "heart.dcm"))
pipeline.source_dir = dcmfolder
# View pipeline
if 0:
from capsul.qt_gui.widgets import PipelineDevelopperView
from PySide import QtGui
app = QtGui.QApplication(sys.argv)
view1 = PipelineDevelopperView(pipeline)
view1.show()
app.exec_()
# Execute the pipeline in the configured study
study_config.run(pipeline)
def pilot_gdti_estimation():
"""
Generalized diffusion tensor estimation
=======================================
"""
# System import
import os
import sys
import datetime
import PySide.QtGui as QtGui
# CAPSUL import
from capsul.qt_gui.widgets import PipelineDevelopperView
from capsul.study_config.study_config import StudyConfig
from capsul.process.loader import get_process_instance
"""
Study configuration
-------------------
We first define the working directory and guarantee this folder exists on
the file system:
"""
working_dir = "/volatile/nsap/clindmri/gdti"
if not os.path.isdir(working_dir):
os.makedirs(working_dir)
"""
And then define the study configuration (here we activate the smart
caching module that will be able to remember which process has already been
processed):
"""
study_config = StudyConfig(modules=["SmartCachingConfig"],
use_smart_caching=True,
output_directory=working_dir)
# Create pipeline
start_time = datetime.datetime.now()
print "Start Pipeline Creation", start_time
pipeline = get_process_instance("clindmri.estimation.gdti.xml")
print "Done in {0} seconds.".format(datetime.datetime.now() - start_time)
# View pipeline
if 0:
app = QtGui.QApplication(sys.argv)
view1 = PipelineDevelopperView(pipeline)
view1.show()
app.exec_()
del view1
# Set pipeline input parameters
pipeline.dfile = "/volatile/imagen/dmritest/000000022453/DTI/000000022453s011a1001.nii.gz"
pipeline.bvalfile = "/volatile/imagen/dmritest/000000022453/DTI/000000022453s011a1001.bval"
pipeline.bvecfile = "/volatile/imagen/dmritest/000000022453/DTI/000000022453s011a1001.bvec"
pipeline.order = 2
pipeline.odf = False
print "Done in {0} seconds.".format(datetime.datetime.now() - start_time)
# Execute the pipeline in the configured study
study_config.run(pipeline, verbose=1)
class TestRunProcess(unittest.TestCase):
""" Execute a process.
"""
def test_execution_with_cache(self):
""" Execute a process with cache.
"""
# Create a study configuration
self.output_directory = tempfile.mkdtemp()
self.study_config = StudyConfig(
modules=["SmartCachingConfig"],
use_smart_caching=True,
output_directory=self.output_directory)
# Call the test
self.execution_dummy()
# Rm temporary folder
shutil.rmtree(self.output_directory)
def test_execution_without_cache(self):
""" Execute a process without cache.
"""
# Create a study configuration
self.output_directory = tempfile.mkdtemp()
self.study_config = StudyConfig(
modules=["SmartCachingConfig"],
use_smart_caching=False,
output_directory=self.output_directory)
# Call the test
self.execution_dummy()
# Rm temporary folder
shutil.rmtree(self.output_directory)
def execution_dummy(self):
""" Test to execute DummyProcess.
"""
# Create a process instance
process = DummyProcess()
# Test the cache mechanism
for param in [(1., 2.3), (2., 2.), (1., 2.3)]:
self.study_config.run(process, executer_qc_nodes=False, verbose=1,
f1=param[0], f2=param[1])
self.assertEqual(process.res, param[0] * param[1])
self.assertEqual(
process.output_directory,
os.path.join(self.output_directory, "{0}-{1}".format(
self.study_config.process_counter - 1, process.name)))
class TestRunProcess(unittest.TestCase):
""" Execute a process.
"""
def test_execution_with_cache(self):
""" Execute a process with cache.
"""
# Create a study configuration
self.output_directory = tempfile.mkdtemp()
self.study_config = StudyConfig(modules=["SmartCachingConfig"],
use_smart_caching=True,
output_directory=self.output_directory)
# Call the test
self.execution_dummy()
# Rm temporary folder
shutil.rmtree(self.output_directory)
def test_execution_without_cache(self):
""" Execute a process without cache.
"""
# Create a study configuration
self.output_directory = tempfile.mkdtemp()
self.study_config = StudyConfig(modules=["SmartCachingConfig"],
use_smart_caching=False,
output_directory=self.output_directory)
# Call the test
self.execution_dummy()
# Rm temporary folder
shutil.rmtree(self.output_directory)
def execution_dummy(self):
""" Test to execute DummyProcess.
"""
# Create a process instance
process = get_process_instance(DummyProcess,
output_directory=self.output_directory)
# Test the cache mechanism
for param in [(1., 2.3), (2., 2.), (1., 2.3)]:
self.study_config.run(process,
executer_qc_nodes=False,
verbose=1,
f1=param[0],
f2=param[1])
self.assertEqual(process.res, param[0] * param[1])
self.assertEqual(process.output_directory, self.output_directory)
("Cognitive - Motor","T",
['clicDaudio','clicGaudio','clicDvideo','clicGvideo','calculaudio','calculvideo',
'phrasevideo','phraseaudio'],[-0.25,-0.25,-0.25,-0.25,0.25,0.25,0.25,0.25]),
("Audio Computation - Audio Sentences","T",['calculaudio','phraseaudio'],[1,-1]),
("Video Computation - Video Sentences","T",['calculvideo','phrasevideo'],[1,-1]),
("Computation - Sentences","T",
['calculaudio','calculvideo','phraseaudio','phrasevideo'],[0.5,0.5,-0.5,-0.5]),
("Video - Checkerboard","T",
['clicDvideo','clicGvideo','calculvideo','phrasevideo','damier_H','damier_V'],
[0.25,0.25,0.25,0.25,-0.5,-0.5]),
("Video Sentences - Checkerboard","T",
['phrasevideo','damier_H','damier_V'],[1,-0.5,-0.5]),
("Audio Click - Audio Sentences","T",
['clicDaudio','clicGaudio','phraseaudio'],[0.5,0.5,-1]),
("Video Click - Video Sentences","T",
['clicDvideo','clicGvideo','phrasevideo'],[0.5,0.5,-1]),
]
print "Done in {0} seconds.".format(datetime.datetime.now() - start_time)
# View pipeline
if 0:
app = QtGui.QApplication(sys.argv)
view1 = PipelineDevelopperView(pipeline)
view1.show()
app.exec_()
del view1
# Execute the pipeline in the configured study
study_config.run(pipeline, verbose=1)
def pilot_qa_fmri():
"""
Imports
-------
This code needs 'capsul' and 'mmutils' package in order to instanciate and
execute the pipeline and to get a toy dataset.
These packages are available in the 'neurospin' source list or in pypi.
"""
# Capsul import
from capsul.study_config.study_config import StudyConfig
from capsul.process.loader import get_process_instance
# Mmutils import
from mmutils.toy_datasets import get_sample_data
"""
Parameters
----------
The 'pipeline_name' parameter contains the location of the pipeline XML
description that will perform the DICOMs conversion, and the 'outdir' the
location of the pipeline's results: in this case a temporary directory.
"""
pipeline_name = "mmqa.fmri.fmri_quality_assurance_bbox.xml"
outdir = tempfile.mkdtemp()
"""
Capsul configuration
--------------------
A 'StudyConfig' has to be instantiated in order to execute the pipeline
properly. It enables us to define the results directory through the
'output_directory' attribute, the number of CPUs to be used through the
'number_of_cpus' attributes, and to specify that we want a log of the
processing step through the 'generate_logging'. The 'use_scheduler'
must be set to True if more than 1 CPU is used.
"""
study_config = StudyConfig(number_of_cpus=1,
generate_logging=True,
use_scheduler=True,
output_directory=outdir)
"""
Get the toy dataset
-------------------
The toy dataset is composed of a functional image that is downloaded
if it is necessary throught the 'get_sample_data' function and exported
locally.
"""
localizer_dataset = get_sample_data("localizer_extra")
"""
Pipeline definition
-------------------
The pipeline XML description is first imported throught the
'get_process_instance' method, and the resulting pipeline instance is
parametrized: in this example we decided to set the date in the converted
file name and we set two DICOM directories to be converted in Nifti
format.
"""
pipeline = get_process_instance(pipeline_name)
pipeline.image_file = localizer_dataset.fmri
pipeline.repetition_time = 2.0
pipeline.exclude_volume = []
pipeline.roi_size = 21
pipeline.score_file = os.path.join(outdir, "scores.json")
"""
Pipeline representation
-----------------------
By executing this block of code, a pipeline representation can be
displayed. This representation is composed of boxes connected to each
other.
"""
if 0:
from capsul.qt_gui.widgets import PipelineDevelopperView
from PySide import QtGui
app = QtGui.QApplication(sys.argv)
view1 = PipelineDevelopperView(pipeline)
view1.show()
app.exec_()
"""
Pipeline execution
------------------
Finally the pipeline is eecuted in the defined 'study_config'.
"""
study_config.run(pipeline)
"""
Access the result
-----------------
Display the computed scores
"""
scores_file = pipeline.scores_file
with open(scores_file, "r") as _file:
scores = json.load(_file)
for key, value in scores.iteritems():
print "{0} = {1}".format(key, value)
def pilot_qa_fmri():
"""
Imports
-------
This code needs 'capsul' and 'mmutils' package in order to instanciate and
execute the pipeline and to get a toy dataset.
These packages are available in the 'neurospin' source list or in pypi.
"""
# Capsul import
from capsul.study_config.study_config import StudyConfig
from capsul.process.loader import get_process_instance
# Mmutils import
from mmutils.toy_datasets import get_sample_data
"""
Parameters
----------
The 'pipeline_name' parameter contains the location of the pipeline XML
description that will perform the DICOMs conversion, and the 'outdir' the
location of the pipeline's results: in this case a temporary directory.
"""
pipeline_name = "mmqa.fmri.fmri_quality_assurance_bbox.xml"
outdir = tempfile.mkdtemp()
"""
Capsul configuration
--------------------
A 'StudyConfig' has to be instantiated in order to execute the pipeline
properly. It enables us to define the results directory through the
'output_directory' attribute, the number of CPUs to be used through the
'number_of_cpus' attributes, and to specify that we want a log of the
processing step through the 'generate_logging'. The 'use_scheduler'
must be set to True if more than 1 CPU is used.
"""
study_config = StudyConfig(
number_of_cpus=1,
generate_logging=True,
use_scheduler=True,
output_directory=outdir)
"""
Get the toy dataset
-------------------
The toy dataset is composed of a functional image that is downloaded
if it is necessary throught the 'get_sample_data' function and exported
locally.
"""
localizer_dataset = get_sample_data("localizer_extra")
"""
Pipeline definition
-------------------
The pipeline XML description is first imported throught the
'get_process_instance' method, and the resulting pipeline instance is
parametrized: in this example we decided to set the date in the converted
file name and we set two DICOM directories to be converted in Nifti
format.
"""
pipeline = get_process_instance(pipeline_name)
pipeline.image_file = localizer_dataset.fmri
pipeline.repetition_time = 2.0
pipeline.exclude_volume = []
pipeline.roi_size = 21
pipeline.score_file = os.path.join(outdir, "scores.json")
"""
Pipeline representation
-----------------------
By executing this block of code, a pipeline representation can be
displayed. This representation is composed of boxes connected to each
other.
"""
if 0:
from capsul.qt_gui.widgets import PipelineDevelopperView
from PySide import QtGui
app = QtGui.QApplication(sys.argv)
view1 = PipelineDevelopperView(pipeline)
view1.show()
app.exec_()
"""
Pipeline execution
------------------
Finally the pipeline is eecuted in the defined 'study_config'.
"""
study_config.run(pipeline)
"""
Access the result
-----------------
Display the computed scores
"""
scores_file = pipeline.scores_file
with open(scores_file, "r") as _file:
scores = json.load(_file)
for key, value in scores.iteritems():
print "{0} = {1}".format(key, value)
class CapsulMainWindow(MyQUiLoader):
""" Capsul main window.
"""
def __init__(self, pipeline_menu, ui_file, default_study_config=None):
""" Method to initialize the Capsul main window class.
Parameters
----------
pipeline_menu: hierachic dict
each key is a sub module of the module. Leafs contain a list with
the url to the documentation.
ui_file: str (mandatory)
a filename containing the user interface description
default_study_config: ordered dict (madatory)
some parameters for the study configuration
"""
# Inheritance: load user interface window
MyQUiLoader.__init__(self, ui_file)
# Class parameters
self.pipeline_menu = pipeline_menu
self.pipelines = {}
self.pipeline = None
self.path_to_pipeline_doc = {}
# Define dynamic controls
self.controls = {
QtGui.QAction: ["actionHelp", "actionQuit", "actionBrowse",
"actionLoad", "actionChangeView",
"actionParameters", "actionRun",
"actionStudyConfig", "actionQualityControl"],
QtGui.QTabWidget: ["display", ],
QtGui.QDockWidget: ["dockWidgetBrowse", "dockWidgetParameters",
"dockWidgetStudyConfig", "dockWidgetBoard"],
QtGui.QWidget: ["dock_browse", "dock_parameters",
"dock_study_config", "dock_board"],
QtGui.QTreeWidget: ["menu_treectrl", ],
QtGui.QLineEdit: ["search", ],
}
# Add ui class parameter with the dynamic controls and initialize
# default values
self.add_controls_to_ui()
self.ui.display.setTabsClosable(True)
# Create the study configuration
self.study_config = StudyConfig(default_study_config)
# Create the controller widget associated to the study
# configuration controller
self.study_config_widget = ScrollControllerWidget(
self.study_config, live=True)
self.ui.dockWidgetStudyConfig.setWidget(self.study_config_widget)
# Create the pipeline menu
fill_treectrl(self.ui.menu_treectrl, self.pipeline_menu)
# Signal for window interface
self.ui.actionHelp.triggered.connect(self.onHelpClicked)
self.ui.actionChangeView.triggered.connect(self.onChangeViewClicked)
# Signal for tab widget
self.ui.display.currentChanged.connect(self.onCurrentTabChanged)
self.ui.display.tabCloseRequested.connect(self.onCloseTabClicked)
# Signal for dock widget
self.ui.actionBrowse.triggered.connect(self.onBrowseClicked)
self.ui.actionParameters.triggered.connect(self.onParametersClicked)
self.ui.actionStudyConfig.triggered.connect(self.onStudyConfigClicked)
self.ui.actionQualityControl.triggered.connect(self.onQualityControlClicked)
# Initialize properly the visibility of each dock widget
self.onBrowseClicked()
self.onParametersClicked()
self.onStudyConfigClicked()
self.onQualityControlClicked()
# Signal for the pipeline creation
self.ui.search.textChanged.connect(self.onSearchClicked)
self.ui.menu_treectrl.currentItemChanged.connect(
self.onTreeSelectionChanged)
self.ui.actionLoad.triggered.connect(self.onLoadClicked)
# Signal for the execution
self.ui.actionRun.triggered.connect(self.onRunClicked)
# Set default values
# Set some tooltips
def show(self):
""" Shows the widget and its child widgets.
"""
self.ui.show()
def add_controls_to_ui(self):
""" Method to find dynamic controls
"""
# Error message template
error_message = "{0} has no attribute '{1}'"
# Got through the class dynamic controls
for control_type, control_item in six.iteritems(self.controls):
# Get the dynamic control name
for control_name in control_item:
# Try to set the control value to the ui class parameter
try:
value = self.ui.findChild(control_type, control_name)
if value is None:
logger.error(error_message.format(
type(self.ui), control_name))
setattr(self.ui, control_name, value)
except:
logger.error(error_message.format(
type(self.ui), control_name))
###########################################################################
# Slots
###########################################################################
def onRunClicked(self):
""" Event to execute the process/pipeline.
"""
self.study_config.run(self.pipeline, executer_qc_nodes=True, verbose=1)
def onBrowseClicked(self):
""" Event to show / hide the browse dock widget.
"""
# Show browse dock widget
if self.ui.actionBrowse.isChecked():
self.ui.dockWidgetBrowse.show()
# Hide browse dock widget
else:
self.ui.dockWidgetBrowse.hide()
def onParametersClicked(self):
""" Event to show / hide the parameters dock widget.
"""
# Show parameters dock widget
if self.ui.actionParameters.isChecked():
self.ui.dockWidgetParameters.show()
# Hide parameters dock widget
else:
self.ui.dockWidgetParameters.hide()
def onStudyConfigClicked(self):
""" Event to show / hide the study config dock widget.
"""
# Show study configuration dock widget
if self.ui.actionStudyConfig.isChecked():
self.ui.dockWidgetStudyConfig.show()
# Hide study configuration dock widget
else:
self.ui.dockWidgetStudyConfig.hide()
def onQualityControlClicked(self):
""" Event to show / hide the board dock widget.
"""
# Create and show board dock widget
if self.ui.actionQualityControl.isChecked():
# Create the board widget associated to the pipeline controller
# Create on the fly in order to get the last status
# ToDo: add callbacks
if self.pipeline is not None:
# board_widget = BoardWidget(
# self.pipeline, parent=self.ui.dockWidgetParameters,
# name="board")
board_widget = ScrollControllerWidget(
self.pipeline, name="outputs", live=True,
hide_labels=False, select_controls="outputs",
disable_controller_widget=True)
#board_widget.setEnabled(False)
self.ui.dockWidgetBoard.setWidget(board_widget)
# Show the board widget
self.ui.dockWidgetBoard.show()
# Hide board dock widget
else:
self.ui.dockWidgetBoard.hide()
def onSearchClicked(self):
""" Event to refresh the menu tree control that contains the pipeline
modules.
"""
# Clear the current tree control
self.ui.menu_treectrl.clear()
# Build the new filtered tree control
fill_treectrl(self.ui.menu_treectrl, self.pipeline_menu,
self.ui.search.text().lower())
def onTreeSelectionChanged(self):
""" Event to refresh the pipeline load button status.
"""
# Get the cuurent item
item = self.ui.menu_treectrl.currentItem()
if item is None:
return
# Check if we have selected a pipeline in the tree and enable / disable
# the load button
url = item.text(2)
if url == "None":
self.ui.actionLoad.setEnabled(False)
else:
self.ui.actionLoad.setEnabled(True)
def onRunStatus(self):
""" Event to refresh the run button status.
When all the controller widget controls are correctly filled, enable
the user to execute the pipeline.
"""
# Get the controller widget
controller_widget = self.ui.dockWidgetParameters.widget().controller_widget
# Get the controller widget status
is_valid = controller_widget.is_valid()
# Depending on the controller widget status enable / disable
# the run button
self.ui.actionRun.setEnabled(is_valid)
def onLoadClicked(self):
""" Event to load and display a pipeline.
"""
# Get the pipeline instance from its string description
item = self.ui.menu_treectrl.currentItem()
description_list = [str(x) for x in [item.text(1), item.text(0)]
if x != ""]
process_description = ".".join(description_list)
self.pipeline = get_process_instance(process_description)
# Create the controller widget associated to the pipeline
# controller
pipeline_widget = ScrollControllerWidget(
self.pipeline, live=True, select_controls="inputs")
self.ui.dockWidgetParameters.setWidget(pipeline_widget)
# Add observer to refresh the run button
controller_widget = pipeline_widget.controller_widget
for control_name, control \
in six.iteritems(controller_widget._controls):
# Unpack the control item
trait, control_class, control_instance, control_label = control
# Add the new callback
control_class.add_callback(self.onRunStatus, control_instance)
# Refresh manually the run button status the first time
self.onRunStatus()
# Store the pipeline documentation root path
self.path_to_pipeline_doc[self.pipeline.id] = item.text(2)
# Store the pipeline instance
self.pipelines[self.pipeline.name] = (
self.pipeline, pipeline_widget)
# Create the widget
widget = PipelineDevelopperView(self.pipeline)
self._insert_widget_in_tab(widget)
# Connect the subpipeline clicked signal to the
# onLoadSubPipelineClicked slot
widget.subpipeline_clicked.connect(self.onLoadSubPipelineClicked)
def onLoadSubPipelineClicked(self, name, sub_pipeline, modifiers):
""" Event to load and display a sub pipeline.
"""
# Store the pipeline instance in class parameters
self.pipeline = self.pipeline.nodes[name].process
# Create the controller widget associated to the sub pipeline
# controller: if the sub pipeline is a ProcessIteration, disable
# the correspondind controller widget since this pipeline is generated
# on the fly an is not directly synchronized with the rest of the
# pipeline.
is_iterative_pipeline = False
if isinstance(self.pipeline, ProcessIteration):
is_iterative_pipeline = True
pipeline_widget = ScrollControllerWidget(
self.pipeline, live=True, select_controls="inputs",
disable_controller_widget=is_iterative_pipeline)
self.ui.dockWidgetParameters.setWidget(pipeline_widget)
# Store the sub pipeline instance
self.pipelines[self.pipeline.name] = (
self.pipeline, pipeline_widget)
# Create the widget
widget = PipelineDevelopperView(self.pipeline)
self._insert_widget_in_tab(widget)
# Connect the subpipeline clicked signal to the
# onLoadSubPipelineClicked slot
widget.subpipeline_clicked.connect(self.onLoadSubPipelineClicked)
def onCloseTabClicked(self, index):
""" Event to close a pipeline view.
"""
# Remove the pipeline from the intern pipeline list
pipeline, pipeline_widget = self.pipelines[
self.ui.display.tabText(index)]
pipeline_widget.close()
pipeline_widget.deleteLater()
del self.pipelines[self.ui.display.tabText(index)]
# Remove the table that contains the pipeline
self.ui.display.removeTab(index)
def onCurrentTabChanged(self, index):
""" Event to refresh the controller controller widget when a new
tab is selected
"""
# If no valid tab index has been passed
if index < 0:
self.ui.actionRun.setEnabled(False)
# A new valid tab is selected
else:
# Get the selected pipeline widget
self.pipeline, pipeline_widget = self.pipelines[
self.ui.display.tabText(index)]
# Set the controller widget associated to the pipeline
# controller
self.ui.dockWidgetParameters.setWidget(pipeline_widget)
# Refresh manually the run button status the first time
self.onRunStatus()
def onHelpClicked(self):
""" Event to display the documentation of the active pipeline.
"""
# Create a dialog box to display the html documentation
win = QtGui.QDialog()
win.setWindowTitle("Pipeline Help")
# Build the pipeline documentation location
# Possible since common tools generate the sphinx documentation
if self.pipeline:
# Generate the url to the active pipeline documentation
path_to_active_pipeline_doc = os.path.join(
self.path_to_pipeline_doc[self.pipeline.id], "generated",
self.pipeline.id.split(".")[1], "pipeline",
self.pipeline.id + ".html")
# Create and fill a QWebView
help = QtWebKit.QWebView()
help.load(QtCore.QUrl(path_to_active_pipeline_doc))
help.show()
# Create and set a layout with the web view
layout = QtGui.QHBoxLayout()
layout.addWidget(help)
win.setLayout(layout)
# Display the window
win.exec_()
# No Pipeline loaded, cant't show the documentation message
# Display a message box
else:
QtGui.QMessageBox.information(
self.ui, "Information", "First load a pipeline!")
def onChangeViewClicked(self):
""" Event to switch between simple and full pipeline views.
"""
# Check if a pipeline has been loaded
if self._is_active_pipeline_valid():
# Check the current display mode
# Case PipelineDevelopperView
if isinstance(self.ui.display.currentWidget(),
PipelineDevelopperView):
# Switch to PipelineUserView display mode
widget = PipelineUserView(self.pipeline)
self._insert_widget_in_tab(widget)
# Case PipelineUserView
else:
# Switch to PipelineDevelopperView display mode
widget = PipelineDevelopperView(self.pipeline)
self._insert_widget_in_tab(widget)
# No pipeline loaded error
else:
logger.error("No active pipeline selected. "
"Have you forgotten to click the load pipeline "
"button?")
#####################
# Private interface #
#####################
def _insert_widget_in_tab(self, widget):
""" Insert a new widget or replace an existing widget.
Parameters
----------
widget: a widget (mandatory)
the widget we want to draw
"""
# Search if the tab corresponding to the widget has already been created
already_created = False
index = 0
# Go through all the tabs
for index in range(self.ui.display.count()):
# Check if we have a match: the tab name is equal to the current
#pipeline name
if (self.ui.display.tabText(index) == self.pipeline.name):
already_created = True
break
# If no match found, add a new tab with the widget
if not already_created:
self.ui.display.addTab(
widget, unicode(self.pipeline.name))
self.ui.display.setCurrentIndex(
self.ui.display.count() - 1)
# Otherwise, replace the widget from the match tab
else:
# Delete the tab
self.ui.display.removeTab(index)
# Insert the new tab
self.ui.display.insertTab(
index, widget, unicode(self.pipeline.name))
# Set the corresponding index
self.ui.display.setCurrentIndex(index)
def _is_active_pipeline_valid(self):
""" Method to ceack that the active pipeline is valid
Returns
-------
is_valid: bool
True if the active pipeline is valid
"""
return self.pipeline is not None
modules=["SmartCachingConfig"],
use_smart_caching=True,
output_directory="/volatile/nsap/catalogue/quality_assurance/")
print "Done in {0} seconds".format(datetime.datetime.now() - start_time)
# Create pipeline
start_time = datetime.datetime.now()
print "Start Pipeline Creation", start_time
pipeline = get_process_instance("mmqa.fmri.fmri_quality_assurance.xml")
print "Done in {0} seconds.".format(datetime.datetime.now() - start_time)
# Set pipeline input parameters
start_time = datetime.datetime.now()
print "Start Parametrization", start_time
localizer_dataset = get_sample_data("localizer")
pipeline.image_file = localizer_dataset.fmri
pipeline.repetition_time = localizer_dataset.TR
print "Done in {0} seconds.".format(datetime.datetime.now() - start_time)
# View pipeline
app = QtGui.QApplication(sys.argv)
view1 = PipelineDevelopperView(pipeline)
view1.show()
app.exec_()
# Execute the pipeline in the configured study
study_config.run(pipeline, executer_qc_nodes=True, verbose=1)
class CapsulMainWindow(MyQUiLoader):
""" Capsul main window.
"""
def __init__(self, pipeline_menu, ui_file, default_study_config=None):
""" Method to initialize the Capsul main window class.
Parameters
----------
pipeline_menu: hierachic dict
each key is a sub module of the module. Leafs contain a list with
the url to the documentation.
ui_file: str (mandatory)
a filename containing the user interface description
default_study_config: ordered dict (madatory)
some parameters for the study configuration
"""
# Inheritance: load user interface window
MyQUiLoader.__init__(self, ui_file)
# Class parameters
self.pipeline_menu = pipeline_menu
self.pipelines = {}
self.pipeline = None
self.path_to_pipeline_doc = {}
# Define dynamic controls
self.controls = {
QtGui.QAction: [
"actionHelp", "actionQuit", "actionBrowse", "actionLoad",
"actionChangeView", "actionParameters", "actionRun",
"actionStudyConfig", "actionQualityControl"
],
QtGui.QTabWidget: [
"display",
],
QtGui.QDockWidget: [
"dockWidgetBrowse", "dockWidgetParameters",
"dockWidgetStudyConfig", "dockWidgetBoard"
],
QtGui.QWidget: [
"dock_browse", "dock_parameters", "dock_study_config",
"dock_board"
],
QtGui.QTreeWidget: [
"menu_treectrl",
],
QtGui.QLineEdit: [
"search",
],
}
# Add ui class parameter with the dynamic controls and initialize
# default values
self.add_controls_to_ui()
self.ui.display.setTabsClosable(True)
# Create the study configuration
self.study_config = StudyConfig(default_study_config)
# Create the controller widget associated to the study
# configuration controller
self.study_config_widget = ScrollControllerWidget(self.study_config,
live=True)
self.ui.dockWidgetStudyConfig.setWidget(self.study_config_widget)
# Create the pipeline menu
fill_treectrl(self.ui.menu_treectrl, self.pipeline_menu)
# Signal for window interface
self.ui.actionHelp.triggered.connect(self.onHelpClicked)
self.ui.actionChangeView.triggered.connect(self.onChangeViewClicked)
# Signal for tab widget
self.ui.display.currentChanged.connect(self.onCurrentTabChanged)
self.ui.display.tabCloseRequested.connect(self.onCloseTabClicked)
# Signal for dock widget
self.ui.actionBrowse.triggered.connect(self.onBrowseClicked)
self.ui.actionParameters.triggered.connect(self.onParametersClicked)
self.ui.actionStudyConfig.triggered.connect(self.onStudyConfigClicked)
self.ui.actionQualityControl.triggered.connect(
self.onQualityControlClicked)
# Initialize properly the visibility of each dock widget
self.onBrowseClicked()
self.onParametersClicked()
self.onStudyConfigClicked()
self.onQualityControlClicked()
# Signal for the pipeline creation
self.ui.search.textChanged.connect(self.onSearchClicked)
self.ui.menu_treectrl.currentItemChanged.connect(
self.onTreeSelectionChanged)
self.ui.actionLoad.triggered.connect(self.onLoadClicked)
# Signal for the execution
self.ui.actionRun.triggered.connect(self.onRunClicked)
# Set default values
# Set some tooltips
def show(self):
""" Shows the widget and its child widgets.
"""
self.ui.show()
def add_controls_to_ui(self):
""" Method to find dynamic controls
"""
# Error message template
error_message = "{0} has no attribute '{1}'"
# Got through the class dynamic controls
for control_type, control_item in six.iteritems(self.controls):
# Get the dynamic control name
for control_name in control_item:
# Try to set the control value to the ui class parameter
try:
value = self.ui.findChild(control_type, control_name)
if value is None:
logger.error(
error_message.format(type(self.ui), control_name))
setattr(self.ui, control_name, value)
except:
logger.error(
error_message.format(type(self.ui), control_name))
###########################################################################
# Slots
###########################################################################
def onRunClicked(self):
""" Event to execute the process/pipeline.
"""
self.study_config.run(self.pipeline, executer_qc_nodes=True, verbose=1)
def onBrowseClicked(self):
""" Event to show / hide the browse dock widget.
"""
# Show browse dock widget
if self.ui.actionBrowse.isChecked():
self.ui.dockWidgetBrowse.show()
# Hide browse dock widget
else:
self.ui.dockWidgetBrowse.hide()
def onParametersClicked(self):
""" Event to show / hide the parameters dock widget.
"""
# Show parameters dock widget
if self.ui.actionParameters.isChecked():
self.ui.dockWidgetParameters.show()
# Hide parameters dock widget
else:
self.ui.dockWidgetParameters.hide()
def onStudyConfigClicked(self):
""" Event to show / hide the study config dock widget.
"""
# Show study configuration dock widget
if self.ui.actionStudyConfig.isChecked():
self.ui.dockWidgetStudyConfig.show()
# Hide study configuration dock widget
else:
self.ui.dockWidgetStudyConfig.hide()
def onQualityControlClicked(self):
""" Event to show / hide the board dock widget.
"""
# Create and show board dock widget
if self.ui.actionQualityControl.isChecked():
# Create the board widget associated to the pipeline controller
# Create on the fly in order to get the last status
# ToDo: add callbacks
if self.pipeline is not None:
# board_widget = BoardWidget(
# self.pipeline, parent=self.ui.dockWidgetParameters,
# name="board")
board_widget = ScrollControllerWidget(
self.pipeline,
name="outputs",
live=True,
hide_labels=False,
select_controls="outputs",
disable_controller_widget=True)
#board_widget.setEnabled(False)
self.ui.dockWidgetBoard.setWidget(board_widget)
# Show the board widget
self.ui.dockWidgetBoard.show()
# Hide board dock widget
else:
self.ui.dockWidgetBoard.hide()
def onSearchClicked(self):
""" Event to refresh the menu tree control that contains the pipeline
modules.
"""
# Clear the current tree control
self.ui.menu_treectrl.clear()
# Build the new filtered tree control
fill_treectrl(self.ui.menu_treectrl, self.pipeline_menu,
self.ui.search.text().lower())
def onTreeSelectionChanged(self):
""" Event to refresh the pipeline load button status.
"""
# Get the cuurent item
item = self.ui.menu_treectrl.currentItem()
if item is None:
return
# Check if we have selected a pipeline in the tree and enable / disable
# the load button
url = item.text(2)
if url == "None":
self.ui.actionLoad.setEnabled(False)
else:
self.ui.actionLoad.setEnabled(True)
def onRunStatus(self):
""" Event to refresh the run button status.
When all the controller widget controls are correctly filled, enable
the user to execute the pipeline.
"""
# Get the controller widget
controller_widget = self.ui.dockWidgetParameters.widget(
).controller_widget
# Get the controller widget status
is_valid = controller_widget.is_valid()
# Depending on the controller widget status enable / disable
# the run button
self.ui.actionRun.setEnabled(is_valid)
def onLoadClicked(self):
""" Event to load and display a pipeline.
"""
# Get the pipeline instance from its string description
item = self.ui.menu_treectrl.currentItem()
description_list = [
str(x) for x in [item.text(1), item.text(0)] if x != ""
]
process_description = ".".join(description_list)
self.pipeline = get_process_instance(process_description)
# Create the controller widget associated to the pipeline
# controller
pipeline_widget = ScrollControllerWidget(self.pipeline,
live=True,
select_controls="inputs")
self.ui.dockWidgetParameters.setWidget(pipeline_widget)
# Add observer to refresh the run button
controller_widget = pipeline_widget.controller_widget
for control_name, control \
in six.iteritems(controller_widget._controls):
# Unpack the control item
trait, control_class, control_instance, control_label = control
# Add the new callback
control_class.add_callback(self.onRunStatus, control_instance)
# Refresh manually the run button status the first time
self.onRunStatus()
# Store the pipeline documentation root path
self.path_to_pipeline_doc[self.pipeline.id] = item.text(2)
# Store the pipeline instance
self.pipelines[self.pipeline.name] = (self.pipeline, pipeline_widget)
# Create the widget
widget = PipelineDevelopperView(self.pipeline)
self._insert_widget_in_tab(widget)
# Connect the subpipeline clicked signal to the
# onLoadSubPipelineClicked slot
widget.subpipeline_clicked.connect(self.onLoadSubPipelineClicked)
def onLoadSubPipelineClicked(self, name, sub_pipeline, modifiers):
""" Event to load and display a sub pipeline.
"""
# Store the pipeline instance in class parameters
self.pipeline = self.pipeline.nodes[name].process
# Create the controller widget associated to the sub pipeline
# controller: if the sub pipeline is a ProcessIteration, disable
# the correspondind controller widget since this pipeline is generated
# on the fly an is not directly synchronized with the rest of the
# pipeline.
is_iterative_pipeline = False
if isinstance(self.pipeline, ProcessIteration):
is_iterative_pipeline = True
pipeline_widget = ScrollControllerWidget(
self.pipeline,
live=True,
select_controls="inputs",
disable_controller_widget=is_iterative_pipeline)
self.ui.dockWidgetParameters.setWidget(pipeline_widget)
# Store the sub pipeline instance
self.pipelines[self.pipeline.name] = (self.pipeline, pipeline_widget)
# Create the widget
widget = PipelineDevelopperView(self.pipeline)
self._insert_widget_in_tab(widget)
# Connect the subpipeline clicked signal to the
# onLoadSubPipelineClicked slot
widget.subpipeline_clicked.connect(self.onLoadSubPipelineClicked)
def onCloseTabClicked(self, index):
""" Event to close a pipeline view.
"""
# Remove the pipeline from the intern pipeline list
pipeline, pipeline_widget = self.pipelines[self.ui.display.tabText(
index)]
pipeline_widget.close()
pipeline_widget.deleteLater()
del self.pipelines[self.ui.display.tabText(index)]
# Remove the table that contains the pipeline
self.ui.display.removeTab(index)
def onCurrentTabChanged(self, index):
""" Event to refresh the controller controller widget when a new
tab is selected
"""
# If no valid tab index has been passed
if index < 0:
self.ui.actionRun.setEnabled(False)
# A new valid tab is selected
else:
# Get the selected pipeline widget
self.pipeline, pipeline_widget = self.pipelines[
self.ui.display.tabText(index)]
# Set the controller widget associated to the pipeline
# controller
self.ui.dockWidgetParameters.setWidget(pipeline_widget)
# Refresh manually the run button status the first time
self.onRunStatus()
def onHelpClicked(self):
""" Event to display the documentation of the active pipeline.
"""
# Create a dialog box to display the html documentation
win = QtGui.QDialog()
win.setWindowTitle("Pipeline Help")
# Build the pipeline documentation location
# Possible since common tools generate the sphinx documentation
if self.pipeline:
# Generate the url to the active pipeline documentation
path_to_active_pipeline_doc = os.path.join(
self.path_to_pipeline_doc[self.pipeline.id], "generated",
self.pipeline.id.split(".")[1], "pipeline",
self.pipeline.id + ".html")
# Create and fill a QWebView
help = QtWebKit.QWebView()
help.load(QtCore.QUrl(path_to_active_pipeline_doc))
help.show()
# Create and set a layout with the web view
layout = QtGui.QHBoxLayout()
layout.addWidget(help)
win.setLayout(layout)
# Display the window
win.exec_()
# No Pipeline loaded, cant't show the documentation message
# Display a message box
else:
QtGui.QMessageBox.information(self.ui, "Information",
"First load a pipeline!")
def onChangeViewClicked(self):
""" Event to switch between simple and full pipeline views.
"""
# Check if a pipeline has been loaded
if self._is_active_pipeline_valid():
# Check the current display mode
# Case PipelineDevelopperView
if isinstance(self.ui.display.currentWidget(),
PipelineDevelopperView):
# Switch to PipelineUserView display mode
widget = PipelineUserView(self.pipeline)
self._insert_widget_in_tab(widget)
# Case PipelineUserView
else:
# Switch to PipelineDevelopperView display mode
widget = PipelineDevelopperView(self.pipeline)
self._insert_widget_in_tab(widget)
# No pipeline loaded error
else:
logger.error("No active pipeline selected. "
"Have you forgotten to click the load pipeline "
"button?")
#####################
# Private interface #
#####################
def _insert_widget_in_tab(self, widget):
""" Insert a new widget or replace an existing widget.
Parameters
----------
widget: a widget (mandatory)
the widget we want to draw
"""
# Search if the tab corresponding to the widget has already been created
already_created = False
index = 0
# Go through all the tabs
for index in range(self.ui.display.count()):
# Check if we have a match: the tab name is equal to the current
#pipeline name
if (self.ui.display.tabText(index) == self.pipeline.name):
already_created = True
break
# If no match found, add a new tab with the widget
if not already_created:
self.ui.display.addTab(widget, unicode(self.pipeline.name))
self.ui.display.setCurrentIndex(self.ui.display.count() - 1)
# Otherwise, replace the widget from the match tab
else:
# Delete the tab
self.ui.display.removeTab(index)
# Insert the new tab
self.ui.display.insertTab(index, widget,
unicode(self.pipeline.name))
# Set the corresponding index
self.ui.display.setCurrentIndex(index)
def _is_active_pipeline_valid(self):
""" Method to ceack that the active pipeline is valid
Returns
-------
is_valid: bool
True if the active pipeline is valid
"""
return self.pipeline is not None
def pilot_dcm2nii():
"""
Imports
-------
This code needs 'capsul' and 'mmutils' package in order to instanciate and
execute the pipeline and to get a toy dataset.
These packages are available in the 'neurospin' source list or in pypi.
"""
import os
import sys
import shutil
import tempfile
from capsul.study_config.study_config import StudyConfig
from capsul.process.loader import get_process_instance
from mmutils.toy_datasets import get_sample_data
"""
Parameters
----------
The 'pipeline_name' parameter contains the location of the pipeline XML
description that will perform the DICOMs conversion, and the 'outdir' the
location of the pipeline's results: in this case a temporary directory.
"""
pipeline_name = "dcmio.dcmconverter.dcm_to_nii.xml"
outdir = tempfile.mkdtemp()
"""
Capsul configuration
--------------------
A 'StudyConfig' has to be instantiated in order to execute the pipeline
properly. It enables us to define the results directory through the
'output_directory' attribute, the number of CPUs to be used through the
'number_of_cpus' attributes, and to specify that we want a log of the
processing step through the 'generate_logging'. The 'use_scheduler'
must be set to True if more than 1 CPU is used.
"""
study_config = StudyConfig(
modules=[],
output_directory=outdir,
number_of_cpus=1,
generate_logging=True,
use_scheduler=True)
"""
Get the toy dataset
-------------------
The toy dataset is composed of a 3D heart dicom image that is downloaded
if it is necessary throught the 'get_sample_data' function and exported
locally in a 'heart.dcm' file.
"""
dicom_dataset = get_sample_data("dicom")
dcmfolder = os.path.join(outdir, "dicom")
if not os.path.isdir(dcmfolder):
os.makedirs(dcmfolder)
shutil.copy(dicom_dataset.barre, os.path.join(dcmfolder, "heart.dcm"))
"""
Pipeline definition
-------------------
The pipeline XML description is first imported throught the
'get_process_instance' method, and the resulting pipeline instance is
parametrized: in this example we decided to set the date in the converted
file name and we set two DICOM directories to be converted in Nifti
format.
"""
pipeline = get_process_instance(pipeline_name)
pipeline.date_in_filename = True
pipeline.dicom_directories = [dcmfolder, dcmfolder]
pipeline.additional_informations = [[("Provided by", "Neurospin@2015")],
[("Provided by", "Neurospin@2015"),
("TR", "1500")]]
pipeline.dcm_tags = [("TR", [("0x0018", "0x0080")]),
("TE", [("0x0018", "0x0081")])]
"""
Pipeline representation
-----------------------
By executing this block of code, a pipeline representation can be
displayed. This representation is composed of boxes connected to each
other.
"""
if 0:
from capsul.qt_gui.widgets import PipelineDevelopperView
from PySide import QtGui
app = QtGui.QApplication(sys.argv)
view1 = PipelineDevelopperView(pipeline)
view1.show()
app.exec_()
"""
Pipeline execution
------------------
Finally the pipeline is eecuted in the defined 'study_config'.
"""
study_config.run(pipeline)
"""
Access the result
-----------------
The 'nibabel' package is used to load the generated images. We display the
numpy array shape and the stored repetiton and echo times: in order
to load the 'descrip' image field we use the 'json' package.
"""
import json
import copy
import nibabel
generated_images = pipeline.filled_converted_files
for fnames in generated_images:
print(">>>", fnames, "...")
im = nibabel.load(fnames[0])
print("shape=", im.get_data().shape)
header = im.get_header()
a = str(header["descrip"])
a = a.strip()
description = json.loads(copy.deepcopy(a))
print("TE=", description["TE"])
print("TR=", description["TR"])
print("Provided by=", description["Provided by"])
def pilot_dcm2nii():
"""
Imports
-------
This code needs 'capsul' and 'mmutils' package in order to instanciate and
execute the pipeline and to get a toy dataset.
These packages are available in the 'neurospin' source list or in pypi.
"""
import os
import sys
import shutil
import tempfile
from capsul.study_config.study_config import StudyConfig
from capsul.process.loader import get_process_instance
from mmutils.toy_datasets import get_sample_data
"""
Parameters
----------
The 'pipeline_name' parameter contains the location of the pipeline XML
description that will perform the DICOMs conversion, and the 'outdir' the
location of the pipeline's results: in this case a temporary directory.
"""
pipeline_name = "dcmio.dcmconverter.dcm_to_nii.xml"
outdir = tempfile.mkdtemp()
"""
Capsul configuration
--------------------
A 'StudyConfig' has to be instantiated in order to execute the pipeline
properly. It enables us to define the results directory through the
'output_directory' attribute, the number of CPUs to be used through the
'number_of_cpus' attributes, and to specify that we want a log of the
processing step through the 'generate_logging'. The 'use_scheduler'
must be set to True if more than 1 CPU is used.
"""
study_config = StudyConfig(modules=[],
output_directory=outdir,
number_of_cpus=1,
generate_logging=True,
use_scheduler=True)
"""
Get the toy dataset
-------------------
The toy dataset is composed of a 3D heart dicom image that is downloaded
if it is necessary throught the 'get_sample_data' function and exported
locally in a 'heart.dcm' file.
"""
dicom_dataset = get_sample_data("dicom")
dcmfolder = os.path.join(outdir, "dicom")
if not os.path.isdir(dcmfolder):
os.makedirs(dcmfolder)
shutil.copy(dicom_dataset.barre, os.path.join(dcmfolder, "heart.dcm"))
"""
Pipeline definition
-------------------
The pipeline XML description is first imported throught the
'get_process_instance' method, and the resulting pipeline instance is
parametrized: in this example we decided to set the date in the converted
file name and we set two DICOM directories to be converted in Nifti
format.
"""
pipeline = get_process_instance(pipeline_name)
pipeline.date_in_filename = True
pipeline.dicom_directories = [dcmfolder, dcmfolder]
pipeline.additional_informations = [[("Provided by", "Neurospin@2015")],
[("Provided by", "Neurospin@2015"),
("TR", "1500")]]
pipeline.dcm_tags = [("TR", [("0x0018", "0x0080")]),
("TE", [("0x0018", "0x0081")])]
"""
Pipeline representation
-----------------------
By executing this block of code, a pipeline representation can be
displayed. This representation is composed of boxes connected to each
other.
"""
if 0:
from capsul.qt_gui.widgets import PipelineDevelopperView
from PySide import QtGui
app = QtGui.QApplication(sys.argv)
view1 = PipelineDevelopperView(pipeline)
view1.show()
app.exec_()
"""
Pipeline execution
------------------
Finally the pipeline is eecuted in the defined 'study_config'.
"""
study_config.run(pipeline)
"""
Access the result
-----------------
The 'nibabel' package is used to load the generated images. We display the
numpy array shape and the stored repetiton and echo times: in order
to load the 'descrip' image field we use the 'json' package.
"""
import json
import copy
import nibabel
generated_images = pipeline.filled_converted_files
for fnames in generated_images:
print(">>>", fnames, "...")
im = nibabel.load(fnames[0])
print("shape=", im.get_data().shape)
header = im.get_header()
a = str(header["descrip"])
a = a.strip()
description = json.loads(copy.deepcopy(a))
print("TE=", description["TE"])
print("TR=", description["TR"])
print("Provided by=", description["Provided by"])
def pilot_gdti_estimation():
"""
Generalized diffusion tensor estimation
=======================================
"""
# System import
import os
import sys
import datetime
import PySide.QtGui as QtGui
# CAPSUL import
from capsul.qt_gui.widgets import PipelineDevelopperView
from capsul.study_config.study_config import StudyConfig
from capsul.process.loader import get_process_instance
"""
Study configuration
-------------------
We first define the working directory and guarantee this folder exists on
the file system:
"""
working_dir = "/volatile/nsap/clindmri/gdti"
if not os.path.isdir(working_dir):
os.makedirs(working_dir)
"""
And then define the study configuration (here we activate the smart
caching module that will be able to remember which process has already been
processed):
"""
study_config = StudyConfig(
modules=["SmartCachingConfig"],
use_smart_caching=True,
output_directory=working_dir)
# Create pipeline
start_time = datetime.datetime.now()
print "Start Pipeline Creation", start_time
pipeline = get_process_instance("clindmri.estimation.gdti.xml")
print "Done in {0} seconds.".format(datetime.datetime.now() - start_time)
# View pipeline
if 0:
app = QtGui.QApplication(sys.argv)
view1 = PipelineDevelopperView(pipeline)
view1.show()
app.exec_()
del view1
# Set pipeline input parameters
pipeline.dfile = "/volatile/imagen/dmritest/000000022453/DTI/000000022453s011a1001.nii.gz"
pipeline.bvalfile = "/volatile/imagen/dmritest/000000022453/DTI/000000022453s011a1001.bval"
pipeline.bvecfile = "/volatile/imagen/dmritest/000000022453/DTI/000000022453s011a1001.bvec"
pipeline.order = 2
pipeline.odf = False
print "Done in {0} seconds.".format(datetime.datetime.now() - start_time)
# Execute the pipeline in the configured study
study_config.run(pipeline, verbose=1)
def pilot_fsl_preproc():
"""
FSL preprocessings
==================
"""
# System import
import os
import sys
import datetime
import PySide.QtGui as QtGui
# CAPSUL import
from capsul.qt_gui.widgets import PipelineDevelopperView
from capsul.study_config.study_config import StudyConfig
from capsul.process.loader import get_process_instance
"""
Study configuration
-------------------
We first define the working directory and guarantee this folder exists on
the file system:
"""
working_dir = "/volatile/nsap/clindmri/fslpreproc"
if not os.path.isdir(working_dir):
os.makedirs(working_dir)
"""
And then define the study configuration (here we activate the smart
caching module that will be able to remember which process has already been
processed):
"""
study_config = StudyConfig(
modules=["SmartCachingConfig", "FSLConfig", "MatlabConfig",
"SPMConfig", "NipypeConfig"],
use_smart_caching=True,
fsl_config="/etc/fsl/4.1/fsl.sh",
use_fsl=True,
output_directory=working_dir)
# Create pipeline
start_time = datetime.datetime.now()
print "Start Pipeline Creation", start_time
pipeline = get_process_instance("clindmri.preproc.fsl_preproc.xml")
print "Done in {0} seconds.".format(datetime.datetime.now() - start_time)
# View pipeline
if 0:
app = QtGui.QApplication(sys.argv)
view1 = PipelineDevelopperView(pipeline)
view1.show()
app.exec_()
del view1
# Set pipeline input parameters
pipeline.dfile = "/volatile/imagen/dmritest/000000022453/DTI/000000022453s011a1001.nii.gz"
pipeline.bvalfile = "/volatile/imagen/dmritest/000000022453/DTI/000000022453s011a1001.bval"
pipeline.bvecfile = "/volatile/imagen/dmritest/000000022453/DTI/000000022453s011a1001.bvec"
print "Done in {0} seconds.".format(datetime.datetime.now() - start_time)
#print pipeline.nodes["eddy"].process._nipype_interface.inputs
print pipeline.nodes["eddy"].process._nipype_interface.cmdline
# Execute the pipeline in the configured study
study_config.run(pipeline, verbose=1)
